Article Article
Nondestructive Evaluation 4.0: Ultrasonic Intelligent Nondestructive Testing and Evaluation for Composites

With the continuous promotion of Industry 4.0, the mass production of composites will be entering Industry 4.0 in the near future. With respect to quality control, nondestructive testing and evaluation (NDT&E) will become nondestructive testing and evaluation (NDE) 4.0 to seamlessly connect with Industry 4.0. Therefore, it is essential to develop innovative NDT&E methods and techniques for NDE 4.0. Although there are many facets of NDE 4.0, intelligent nondestructive testing and evaluation (iNDT&E) can be considered as a technical core of NDE 4.0. Therefore, we propose the development of iNDT&E techniques for composites. In this study, the development processes for NDE 1.0–4.0 are analyzed. The basic connotations of iNDT&E are discussed, and the basic framework and technical elements of iNDT&E are examined. As combined with ultrasonic iNDT&E for composites, the basic progress in iNDT&E techniques is reviewed from the perspectives of ultrasonic equipment, technology and methods, processes, and result evaluations and standards. Future development directions are presented for the iNDT&E techniques in NDE 4.0 for composites.

DOI: https://doi.org/10.1080/09349847.2020.1826613

References

C.-H. Cheng et al., Semantic degrees for industrie 4.0 engineering: deciding on the degree of semantic formalization to select appropriate technologies, Proceeding 10th Joint Meeting on Foundations of Software Engineering (ESEC.FSE 15), Italy, 2015, pp.1010–1013.

M. Hermann, T. Pentek, and B. Otto, Design principles for industrie 4.0 scenarios: a literature review, in Proceeding 49th Hawaii International Conference on System Sciences (HICSS), USA, 2016, pp.3928–3937.

H. Lasi et al., Industry 4.0. Bus Inf Syst Eng. 6(4), 239–242 (2014). DOI:10.1007/s12599-014-0334-4.

R. Schmidt et al., Industry 4.0-potentials for creating smart products: empirical research results. Int Conf Bus Inf Syst. 208, 16–27 (2015 Jun).

J. Posada, et al., Visual computing as a key enabling technology for industrie 4.0 and industrial internet. IEEE Comput. Graph. Appl. 35(2), (2015). DOI:10.1109/MCG.2015.45.

N. G. H. Meyendorf et al., NDE 4.0 - NDE for the 21st Century – the internet of things and cyber physical systems will revolutionize NDE, 15th Asia Pacific Conference for Non-Destructive Testing (APCNDT2017), Singapore.

7. D. Chakraborty and M. E. McGovern, NDE 4.0: smart NDE, 2019 IEEE International Conference on Prognostics and Health Management (ICPHM), https://ieeexplore.ieee.org/search/searchresult.jsp?newsearch=true&queryText=NDE%204.0:%20Smart%20NDE.

J. Vrana and R. Singh, The NDE 4.0: Key Challenges, Use Cases, and Adaption, arXiv:2003.07773.

R. Prakash, Non-destructive testing of composites. Composites. 11(4), 217–224 (1980 October). DOI:10.1016/0010-4361(80)90428-0.

S. Kumar and D. Mahto, Recent trends in industrial and other engineering applications of non destructive testing: a review. Int J Sci Eng Res. 4(9), 1–12 (2013 September).

I. Cornwell and A. McNab, Towards automated interpretation of ultrasonic NDT data.

NDT E Int. 32(2), 101–107 (1999 March). DOI:10.1016/S0963-8695(98)00016-4.

N. Brierley, T. Tippetts, and P. Cawley, Improving the reliability of automated non-destructive inspection. DOI:10.1063/1.4716433.

J. W. Shi, S. P. Liu, and G. L. Xun, J Aeronautical Mater 40(2), 89–99 (2020).

S. Yella, M. Dougherty, and N. K. Gupta, Artificial Intelligence techniques for the automatic interpretation of data from non-destructive testing. Insight Non Destruct Testing Cond Monitoring. 48(1), 10–20 (2006 January). DOI:10.1784/insi.2006.48.1.10.

N. Brierley, T. Tippetts, and P. Cawley, Data fusion for automated non-destructive inspection. Proc R Soc Math Phys Eng Sci. 470(2167), 20140167 (2014 July). DOI:10.1098/rspa.2014.0167.

T. M. Meksen, B. Boudraa, and M. Boudraa, A method to improve and automate flat defect detection during ultrasonic inspection. Int J Adapt Control Signal Process. 26(5), (2012 May). DOI:10.1002/acs.1289.

R. Asmatulu et al., Investigating the effects of surface treatments on adhesion properties of protective coatings on carbon fiber-reinforced composite laminates. Surf. Coat. Technol. 380(25), 125006 (2019 December). DOI:10.1016/j.surfcoat.2019.125006.

I. M. Alarifi et al., J. Braz. Soc. Mech. Sci. Eng 41(8), 345 (2019). DOI: 10.1007/s40430-019-1841-5.

S. Kasaragadda et al., Microsystem Technol. 1–13 (2019). DOI: 10.1007/s00542-019-04507-y.

M. Akermi et al., J. Mol. Liq. 287, 110963 (2019). DOI: 10.1016/j.molliq.2019.110963.

G. Chinni et al., Investigating the effects of process parameters on microalgae growth, lipid extraction, and stable nanoemulsion productions. J. Mol. Liq. 291(1), 111308 (2019 October). DOI:10.1016/j.molliq.2019.111308.

V. Vishal et al., Mater. Today 16(Part 2), 579–583 (2019). DOI: 10.1016/j.matpr.2019.05.131.

L. Pieczonka, F. Aymerich, and W. J. Staszewski, Proc. Eng 88, 216–221 (2014). DOI: 10.1016/j.proeng.2014.11.147.

C. John Aldrin, Intelligence augmentation and Human Machine Interface (HMI) best practices for NDT 4.0 reliability, ASNT Annual Conference, Westgate Las Vegas Resort & Casino, Wsetgs Las Vegas, Nevada, November 2019, DOI:10.13140/RG.2.2.28149.93929.

T. J. Barry et al., Defect characterisation in laminar composite structures using ultrasonic techniques and artificial neural networks. J. Compos. Mater. 12 (2015 May 7). DOI:10.1177/0021998315584651.

R. Olsson et al., Compos. Sci. Technol. 63(2), 199–209 (2003). DOI: 10.1016/S0266-3538(02)00193-8.

V. V. Bolotin, Mech. Composite. Mater 37(6), 367–380 (2001).

J. Andersons, M. Hojo, and S. Ochiai, Int. J. Fatigue 26(6), 597–604 (2004). DOI: 10.1016/j.ijfatigue.2003.10.016.

A. Sjogren and L. E. Asp, Int. J. Fatigue 24(2–4), 179–184 (2002). DOI: 10.1016/S0142-1123(01)00071-8.

L. Melin, J. Schon, and T. Nyman, Fatigue testing and buckling characteristics of impacted composite specimens. Int J Fatigue. 24(2–4), 263–272 (2002 February – April). DOI:10.1016/S0142-1123(01)00081-0.

W. M. Ostachowicz et al., Proc. Eng. Proc. Eng. 188, 241–247 (2017). DOI: 10.1016/j.proeng.2017.04.480.

R. Adams and P. Cawley, NDT Int. 21(4), 208–222 (1988). DOI: 10.1016/0308-9126(88)90333-1.

M. V. Hosur et al., NDT E Int. 31(5), 359–374 (1998). DOI: 10.1016/S0963-8695(97)00053-4.

T. D’Orazio et al., Automatic ultrasonic inspection for internal defect detection in composite materials. NDT E Int.. 41(2), 145–154 (2008 March). DOI:10.1016/j.ndteint.2007.08.001

C. Garnier, et al., The detection of aeronautical defects in situ on composite structures using Non Destructive Testing [J]. Composite Struct.. 93(5), 1328–1336 (2011). DOI:10.1016/j.compstruct.2010.10.017.

E. BarraNdiaye, P. Maréchal, and H. Dufl, Adhesion characterization and defect sizing of sandwich honeycomb composites. Ultrasonics. 62, 103–111 (2015). DOI:10.1016/j.ultras.2015.05.007

A. Katunin, K. Dragan, and M. Dziendzikowski, Composite Struct. 127, 1–9 (2015). DOI: 10.1016/j.compstruct.2015.02.080.

B. B. Djordjevic, ultrasonic characterization of advanced composite materials, The 10th International Conference of the Slovenian Society for Non-Destructive Testing, Application of Contemporary Non-Destructive Testing in Engineering, September 1-3, 2009, Ljubljana, Slovenia.

K. Maslov et al., Compos. Sci. Technol. 60(12–13), 2185–2190 (2000). DOI: 10.1016/S0266-3538(00)00013-0.

F. F. Liu et al., Composites Part B 181, 107534 (2020). DOI: https://doi.org/10.1016/j.compositesb.2019.107534.

R. Russell-Floyd and M. Phillips, NDT Int. 21(4), 247–257 (1988). DOI: 10.1016/0308-9126(88)90338-0.

S. Ravishankar and C. Murthy, NDT E Int. 33(5), 341–348 (2000). DOI: 10.1016/S0963-8695(99)00059-6.

A. Katunin, Vibration-based spatial damage identification in honeycomb-core sandwich composite structures using wavelet analysis [J]. Composite Struct.. 118, 385–391 (2014). DOI:10.1016/j.compstruct.2014.08.010.

J. Vrana et al., Non-Destructive Testing of Forgings on the Way to Industry 4.0, ASNT Annual Conference 2018, George R. Brown Convetion Center, Houston, Texas, October 2018.

S. Sambath, P. Nagaraj, and N. Selvakumar, Automatic defect classification in ultrasonic NDT using artificial intelligence. J Nondestruct Eval. 30(1), 20–28 (2011 March). DOI:10.1007/s10921-010-0086-0.

R. Raišutis and O. Tumsys, Materials 13(7), 1689 (2020). DOI: 10.3390/ma13071689.

M. S. Rahman, A. Haryono, and M. A. Abou-Khousa, Microwave non-destructive evaluation of glass reinforced epoxy and high density polyethylene pipes. J Nondestruct Eval. 39(1), (2020 March). DOI:10.1007/s10921-020-00669-2.

S. P. Liu, Aeronautical Manufact. Technol. 5, 26–28 (1991).

Y. B. Huang et al., A pixel-level method for multiple imaging sensor data fusion through artificial neural networks. Advan Nat Sci. 4 (1) (2011). DOI: 10.3968/j.ans.1715787020110401.001.

B. H. Shan et al., Development of a portable ultrasonic phased array inspection imaging apparatus for NDT, Proc. SPIE 7983, Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2011, 79830F (18 April 2011), San Diego, California, United States; 10.1117/12.880257.

C. Z. Guo et al. Ultrasonic non-destructive testing system of semi-enclosed workpiece with dual-robot testing system. Sensors (Basel). 19(15), 3359 (2019 Aug). DOI:10.3390/s19153359.

C. Mineo, S. G. Pierce, and B. Wright, PAUT inspection of complex-shaped composite materials through six DOFs robotic manipulators. Insight Non Destruct. Testing Cond. Monitoring. 57(3), 161–166 (2015 March). DOI:10.1784/insi.2014.57.3.161.

S. Furuya, et al., Imagification technology and deep learning accelerating defect detection in non-destructive testing for wind turbine blades. Fujitsu Sci. Tech. J.. 55(2), 23–29 (2019).

M. X. Zhang et al., Onset detection of ultrasonic signals for the testing of concrete foundation piles by coupled continuous wavelet transform and machine learning algorithms. Advan. Eng. Inf. 43, 101034 (2020 January). DOI:10.1016/j.aei.2020.101034.

J. C. Aldrin and D. S. Forsyth, Demonstration of using signal feature extraction and deep learning neural networks with ultrasonic data for detecting challenging discontinuities in composite panels, AIP Conference Proceedings 2102, 020012 (2019). DOI:10.1063/1.5099716.

Z. Q. Su, L. Ye, and Y. Lu, J. Sound Vib. 295(3–5), 753–780 (2006). DOI: 10.1016/j.jsv.2006.01.020.

J. B. Santos and F. Perdigão, Automatic defects classification — a contribution. NDT E Int. 34(5), 313–318 (2001 July). DOI:10.1016/S0963-8695(00)00043-8.

A. Katunin, Stone impact damage identification in composite plates using modal data and quincunx wavelet analysis. Arch. Civil Mech. Eng. 15(1), 251–261 (2015 January). DOI:10.1016/j.acme.2014.01.010.

 

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